Prostate cancer growth patterns beyond the Gleason score: entering a new era of comprehensive tumour grading

Unsupervised self-organising map classification of Raman spectra from prostate cell lines uncovers substratified prostate cancer disease states

Prostate cancer is a disease which poses an interesting clinical question: Should it be treated? Only a small subset of prostate cancers are aggressive and require removal and treatment to prevent metastatic spread. However, conventional diagnostics remain challenged to risk-stratify such patients; hence, new methods of approach to biomolecularly sub-classify the disease are needed. Here we use an unsupervised self-organising map approach to analyse live-cell Raman spectroscopy data obtained from prostate cell-lines; our aim is to exemplify this method to sub-stratify, at the single-cell-level, the cancer disease state using high-dimensional datasets with minimal preprocessing. The results demonstrate a new sub-clustering of the prostate cancer cell-line into two groups-protein-rich and lipid-rich sub-cellular components-which we believe to be mechanistically linked. This finding shows the potential for unsupervised machine learning to discover distinct disease-state features for more accurate characterisation of highly heterogeneous prostate cancer. Applications may lead to more targeted diagnoses, prognoses and clinical treatment decisions via molecularly-informed stratification that would benefit patients. A method that could discover distinct disease-state features that are mechanistically linked could also assist in the development of more effective broad-spectrum treatments that simultaneously target linked disease-state processes.

Machine Learning-based Nomograms for Predicting Clinical Stages of Initial Prostate Cancer: A Multicenter Retrospective Study

OBJECTIVE

To construct and externally validate machine learning-based nomograms for predicting progression stages of initial prostate cancer (PCa) using biomarkers and clinicopathologic features.

METHODS

Three hundred sixty-two inpatients diagnosed with PCa at the First Affiliated Hospital were randomly assigned to training and testing sets in a 3:7 ratio, while 136 PCa patients from People's Hospital formed the external validation set. Imaging and clinicopathologic information were collected. Optimal features distinguishing advanced prostate cancer (APC) and metastatic PCa (mPCa) were identified through logistic regression (LR). ML algorithms were employed to build and compare ML models. The best-performing algorithm established models for PCa progression stage. Models performance was evaluated using metrics, ROC curves, calibration, and decision curve analysis (DCA) in training, testing, and external validation sets.

RESULTS

Following LR analyses, PSA (P = .001), maximum tumor diameter (P = .026), Gleason score (P <.001), and RNF41 (P <.001) were optimal features for predicting APC, while ALP (P <.001), PSA (P <.001), and GS score (P = .024) were for mPCa. Among ML models, the LR models exhibited superior performance. Consequently, the LR algorithm was used for the APC-risk-nomogram and mPCa-risk-nomogram construction, with AUC values of 0.848, 0.814, 0.810, and 0.940, 0.913, 0.910, in the training, testing, and external validation sets, respectively. Calibration and DCA curves affirmed nomograms' consistency and net benefits for clinical decision-making.

CONCLUSION

In summary, ML-based APC-risk-nomogram and mPCa-risk-nomogram exhibit outstanding predictive performance for PCa progression stages. These nomograms can assist clinicians in finely categorizing newly diagnosed PCa patients, facilitating personalized treatment plans and prognosis assessment.

Cribriform Pattern Is a Predictive Factor of PSA Recurrence in Patients Receiving Radiotherapy After Prostatectomy

Background/Aim

In prostate cancer, robotic total prostatectomy is a popular treatment modality. However, prostate-specific antigen (PSA) recurrence after prostate cancer surgery remains a concern. Salvage radiotherapy is commonly used to treat PSA recurrence, but the recurrence rate after salvage radiotherapy is high, highlighting the need for better predictive markers. This study aimed to retrospectively evaluate the association between cribriform pattern and PSA recurrence in patients receiving radiotherapy after radical prostatectomy.

Patients and Methods

Data of 50 patients who underwent radiotherapy after total prostatectomy between January 2010 and May 2020 were retrospectively evaluated. The median age was 67 years. Among these patients, two cases involved postoperative irradiation, while 48 cases involved salvage irradiation after postoperative PSA recurrence. The median time from surgery to PSA recurrence was 38.3 months. The median radiation dose was 64 Gy in 32 fractions. Three-dimensional conformal radiation therapy was administered in 38 cases and intensity-modulated radiation therapy was used in 12 cases. Combined hormone therapy was administered in 21 cases. PSA levels were measured every 3 months after treatment. Statistical analysis between groups was performed by a t-test.

Results

The median follow-up period after radiotherapy was 31 months. No local recurrences were observed at the prostate bed, and no deaths related to prostate cancer were recorded during follow-up. However, 18 patients (36.0%) had PSA recurrence. The PSA recurrence rate based on the cribriform pattern was 17.6% in the none to moderate group (34 patients) and 75.0% in the severe cribriform pattern group (16 patients). The PSA recurrence rate was significantly higher in patients with a severe invasive cribriform pattern (p=0.001). No significant differences were observed in other histopathological characteristics.

Conclusion

The cribriform pattern in surgical pathology specimens was found to be a useful predictor of PSA recurrence after postoperative radiotherapy.

Pathologic and Short-Term Oncologic Outcomes of Prostate Cancer Patients Following Transvesical Robot-Assisted Radical Prostatectomy

PURPOSE

To study the pathologic and short-term oncological and survival outcomes following Transvesical Single-Port Robot-Assisted Radical Prostatectomy.

MATERIALS AND METHODS

A retrospective review was performed on prospectively collected data on 169 patients with low and intermediate-risks prostate cancer, who either underwent Single-Port Transvesical or Multi-Port Transperitoneal Radical Prostatectomy by a single surgeon between 2015 and 2022. Preoperative clinicopathologic characteristics, as well as final histopathology outcomes, were compared. Univariate Cox proportional hazard analysis was used to evaluate the impact of the surgical approach on biochemical recurrence-free survival within 12 months.

RESULTS

Single-Port Transvesical and Multi-Port Transperitoneal Robotic Radical Prostatectomy were completed in 85 and 84 patients, respectively. Preoperative clinicopathologic features were similar between the 2 groups. In terms of histopathology outcomes, the 2 groups had identical final Gleason Grades, T stage, as well as the rates of adverse pathological features and positive surgical margins (P = >0.05). Despite the lower median number of nodes in the single-port cohort of 2 (0-5) compared to 6 (4-9) in the multi-port cohort (P = <0.001), there remained no statistically significant difference in the rates of lymph node invasion (P = 0.08). At a median follow-up of 12 months, there were no differences in the biochemical recurrence-free survival rates among both groups (P = 0.38). Univariate Cox proportional hazard analysis did not consider surgical approach to be an independent predictor of biochemical recurrence (HR 0.53, 95%CI 0.13-2.23, P = 0.39).

CONCLUSION

In well-selected patients, single-port transvesical robotic radical prostatectomy provided a similar short-term oncologic control as the multi-port approach with similar surgical margin status and 1-year biochemical recurrence rates.

Proposal for an optimised definition of adverse pathology (unfavourable histology) that predicts metastatic risk in prostatic adenocarcinoma independent of grade group and pathological stage

AIMS

Histological grading of prostate cancer is a powerful prognostic tool, but current criteria for grade assignment are not fully optimised. Our goal was to develop and test a simplified histological grading model, based heavily on large cribriform/intraductal carcinoma, with optimised sensitivity for predicting metastatic potential.

METHODS AND RESULTS

Two separate non-overlapping cohorts were identified: a 419-patient post-radical prostatectomy cohort with long term clinical follow-up and a 209-patient post-radical prostatectomy cohort in which all patients had pathologically confirmed metastatic disease. All prostatectomies were re-reviewed for high-risk histological patterns of carcinoma termed 'unfavourable histology'. Unfavourable histology is defined by any classic Gleason pattern 5 component, any large cribriform morphology (> 0.25 mm) or intraductal carcinoma, complex intraluminal papillary architecture, grade 3 stromogenic carcinoma and complex anastomosing cord-like growth. For the outcome cohort, Kaplan-Meier analysis compared biochemical recurrence, metastasis and death between subjects with favourable and unfavourable histology, stratified by pathological stage and grade group. Multivariable Cox proportional hazards models evaluated adding unfavourable histology to the Memorial Sloan Kettering Cancer Center (MSKCC) post-prostatectomy nomogram and stratification by percentage of unfavourable histology. At 15 years unfavourable histology predicted biochemical recurrence, with sensitivity of 93% and specificity of 88%, metastatic disease at 100 and 48% and death at 100 and 46%. Grade group 2 prostate cancers with unfavourable histology were associated with metastasis independent of pathological stage, while those without had no risk. Histological models for prediction of metastasis based on only large cribriform/intraductal carcinoma or increasing diameter of cribriform size improved specificity, but with lower sensitivity. Multivariable Cox proportional hazards models demonstrated that unfavourable histology significantly improved discriminatory power of the MSKCC post-prostatectomy nomogram for biochemical failure (likelihood ratio test P < 0.001). In the retrospective review of a separate RP cohort in which all patients had confirmed metastatic disease, none had unequivocal favourable histology.

CONCLUSIONS

Unfavourable histology at radical prostatectomy is associated with metastatic risk, predicted adverse outcomes better than current grading and staging systems and improved the MSKCC post-prostatectomy nomogram. Most importantly, unfavourable histology stratified grade group 2 prostate cancers into those with and without metastatic potential, independent of stage. While unfavourable histology is driven predominantly by large cribriform/intraductal carcinoma, the recognition and inclusion of other specific architectural patterns add to the sensitivity for predicting metastatic disease. Moreover, a simplified dichotomous model improves communication and could increase implementation.

Nomogram-based prognostic model construction for progression to castration-resistant prostate cancer in patients with high tumor burden and osseous metastatic prostate cancer

This study aims to construct a Nomogram model to predict the risk of developing castration-resistant prostate cancer (CRPC) in patients with high tumor burden (HTB) and osseous metastatic prostate cancer (PCa), and to identify key prognostic factors. A retrospective analysis was conducted on patients with HTB and osseous metastatic PCa treated at The Sixth Affiliated Hospital, School of Medicine, South China University of Technology and the Second Affiliated Hospital of Guangzhou Medical University from January 2018 to February 2022. Patients' baseline data and laboratory indexes were collected. Cox regression analysis identified neural invasion (NI; P<0.001, HR: 2.371, 95% CI: 1.569-3.582), Gleason score (P=0.002, HR: 1.787, 95% CI: 1.241-2.573), initial PSA (P=0.004, HR: 1.677, 95% CI: 1.174-2.396), and lactate dehydrogenase (LDH; P<0.001, HR: 2.729, 95% CI: 1.855-4.014) as significant prognostic factors for progression to CRPC. The constructed Nomogram model exhibited high accuracy in predicting one- and two-year progression to CRPC, with external validation confirming its predictive performance. Time-dependent receiver operating characteristic (ROC) curves indicated that the areas under the curves (AUCs) of the model for one- and two-year progression to CRPC were 0.81 and 0.76, respectively. This model demonstrates high predictive performance, aiding clinical decision-making and providing personalized treatment strategies for patients with HTB and osseous metastatic PCa.

The prognostic value of Dickkopf-3 (Dkk3), TGFB1 and ECM-1 in prostate cancer

Prostate cancer (PCa) is considered one of the most common cancers worldwide. Despite advances in patient diagnosis, management, and risk stratification, 10%-20% of patients progress to castration-resistant disease. Our previous report highlighted a protective role of Dickkopf-3 (DKK3) in PCa stroma. This role was proposed to be mediated through opposing extracellular matrix protein 1 (ECM-1) and TGF-β signalling activity. However, a detailed analysis of the prognostic value of DKK3, ECM-1 and members of the TGF-β signalling pathway in PCa was not thoroughly investigated. In this study, we explored the prognostic value of DKK3, ECM-1 and TGFB1 using a bioinformatical approach through analysis of large publicly available datasets from The Cancer Genome Atlas Program (TGCA) and Pan-Cancer Atlas databases. Our results showed a significant gradual loss of DKK3 expression with PCa progression (p < 0.0001) associated with increased DNA methylation in its promoter region (p < 1.63E-12). In contrast, patients with metastatic lesions showed significantly higher levels of TGFB1 expression compared to primary tumours (p < 0.00001). Our results also showed a marginal association between more advanced tumour stage presented as positive lymph node involvement and low DKK3 mRNA expression (p = 0.082). However, while ECM1 showed no association with tumour stage (p = 0.773), high TGFB1 expression showed a significant association with more advanced stage presented as advanced T3 stage compared to patients with low TGFB1 mRNA expression (p < 0.001). Interestingly, while ECM1 showed no significant association with patient outcome, patients with high DKK3 mRNA expression showed a significant association with favourable outcomes presented as prolonged disease-specific (p = 0.0266), progression-free survival (p = 0.047) and disease-free (p = 0.05). In contrast, high TGFB1 mRNA expression showed a significant association with poor patient outcomes presented as shortened progression-free (p = 0.00032) and disease-free survival (p = 0.0433). Moreover, DKK3, TGFB1 and ECM1 have acted as immune-associated genes in the PCa tumour microenvironment. In conclusion, our findings showed a distinct prognostic value for this three-gene signature in PCa. While both DKK3 and TGFB1 showed a potential role as a clinical marker for PCa stratification, ECM1 showed no significant association with the majority of clinicopathological parameters, which reduce its clinical significance as a reliable prognostic marker.

Differential analysis of histopathological and genetic markers of cancer aggressiveness, and survival difference in EBV-positive and EBV-negative prostate carcinoma

Several studies have shown an association between prostate carcinoma (PCa) and Epstein-Barr virus (EBV); however, none of the studies so far have identified the histopathological and genetic markers of cancer aggressiveness associated with EBV in PCa tissues. In this study, we used previously characterized EBV-PCR-positive (n = 39) and EBV-negative (n = 60) PCa tissues to perform an IHC-based assessment of key histopathological and molecular markers of PCa aggressiveness (EMT markers, AR expression, perineural invasion, and lymphocytic infiltration characterization). Additionally, we investigated the differential expression of key oncogenes, EMT-associated genes, and PCa-specific oncomiRs, in EBV-positive and -negative tissues, using the qPCR array. Finally, survival benefit analysis was also performed in EBV-positive and EBV-negative PCa patients. The EBV-positive PCa exhibited a higher percentage (80%) of perineural invasion (PNI) compared to EBV-negative PCa (67.3%) samples. Similarly, a higher lymphocytic infiltration was observed in EBV-LMP1-positive PCa samples. The subset characterization of T and B cell lymphocytic infiltration showed a trend of higher intratumoral and tumor stromal lymphocytic infiltration in EBV-negative tissues compared with EBV-positive tissues. The logistic regression analysis showed that EBV-positive status was associated with decreased odds (OR = 0.07; p-value < 0.019) of CD3 intratumoral lymphocytic infiltration in PCa tissues. The analysis of IHC-based expression patterns of EMT markers showed comparable expression of all EMT markers, except vimentin, which showed higher expression in EBV-positive PCa tissues compared to EBV-negative PCa tissues. Furthermore, gene expression analysis showed a statistically significant difference (p < 0.05) in the expression of CDH1, AR, CHEK-2, CDKN-1B, and CDC-20 and oncomiRs miR-126, miR-152-3p, miR-452, miR-145-3p, miR-196a, miR-183-3p, and miR-146b in EBV-positive PCa tissues compared to EBV-negative PCa tissues. Overall, the survival proportion was comparable in both groups. The presence of EBV in the PCa tissues results in an increased expression of certain oncogenes, oncomiRs, and EMT marker (vimentin) and a decrease in CD3 ITL, which may be associated with the aggressive forms of PCa.

Contemporary Diagnostic Reporting for Prostatic Adenocarcinoma: Morphologic Aspects, Molecular Correlates, and Management Perspectives

The diagnosis and reporting of prostatic adenocarcinoma have evolved from the classic framework promulgated by Dr Donald Gleason in the 1960s into a complex and nuanced system of grading and reporting that nonetheless retains the essence of his remarkable observations. The criteria for the "Gleason patterns" originally proposed have been continually refined by consensuses in the field, and Gleason scores have been stratified into a patient-friendly set of prognostically validated and widely adopted Grade Groups. One product of this successful grading approach has been the opportunity for pathologists to report diagnoses that signal carefully personalized management, placing the surgical pathologist's interpretation at the center of patient care. At one end of the continuum of disease aggressiveness, personalized diagnostic care means to sub-stratify patients with more indolent disease for active surveillance, while at the other end of the continuum, reporting histologic markers signaling aggression allows sub-stratification of clinically significant disease. Whether contemporary reporting parameters represent deeper nuances of more established ones (eg, new criteria and/or quantitation of Gleason patterns 4 and 5) or represent additional features reported alongside grade (intraductal carcinoma, cribriform patterns of carcinoma), assessment and grading have become more complex and demanding. Herein, we explore these newer reporting parameters, highlighting the state of knowledge regarding morphologic, molecular, and management aspects. Emphasis is made on the increasing value and stakes of histopathologists' interpretations and reporting into current clinical risk stratification and treatment guidelines.

Intra-prostatic tumour evolution, steps in metastatic spread and histogenomic associations revealed by integration of multi-region whole-genome sequencing with histopathological features

BACKGROUND

Extension of prostate cancer beyond the primary site by local invasion or nodal metastasis is associated with poor prognosis. Despite significant research on tumour evolution in prostate cancer metastasis, the emergence and evolution of cancer clones at this early stage of expansion and spread are poorly understood. We aimed to delineate the routes of evolution and cancer spread within the prostate and to seminal vesicles and lymph nodes, linking these to histological features that are used in diagnostic risk stratification.

METHODS

We performed whole-genome sequencing on 42 prostate cancer samples from the prostate, seminal vesicles and lymph nodes of five treatment-naive patients with locally advanced disease. We spatially mapped the clonal composition of cancer across the prostate and the routes of spread of cancer cells within the prostate and to seminal vesicles and lymph nodes in each individual by analysing a total of > 19,000 copy number corrected single nucleotide variants.

RESULTS

In each patient, we identified sample locations corresponding to the earliest part of the malignancy. In patient 10, we mapped the spread of cancer from the apex of the prostate to the seminal vesicles and identified specific genomic changes associated with the transformation of adenocarcinoma to amphicrine morphology during this spread. Furthermore, we show that the lymph node metastases in this patient arose from specific cancer clones found at the base of the prostate and the seminal vesicles. In patient 15, we observed increased mutational burden, altered mutational signatures and histological changes associated with whole genome duplication. In all patients in whom histological heterogeneity was observed (4/5), we found that the distinct morphologies were located on separate branches of their respective evolutionary trees.

CONCLUSIONS

Our results link histological transformation with specific genomic alterations and phylogenetic branching. These findings have implications for diagnosis and risk stratification, in addition to providing a rationale for further studies to characterise the genetic changes causally linked to morphological transformation. Our study demonstrates the value of integrating multi-region sequencing with histopathological data to understand tumour evolution and identify mechanisms of prostate cancer spread.

Proteomic-based stratification of intermediate-risk prostate cancer patients

Gleason grading is an important prognostic indicator for prostate adenocarcinoma and is crucial for patient treatment decisions. However, intermediate-risk patients diagnosed in the Gleason grade group (GG) 2 and GG3 can harbour either aggressive or non-aggressive disease, resulting in under- or overtreatment of a significant number of patients. Here, we performed proteomic, differential expression, machine learning, and survival analyses for 1,348 matched tumour and benign sample runs from 278 patients. Three proteins (F5, TMEM126B, and EARS2) were identified as candidate biomarkers in patients with biochemical recurrence. Multivariate Cox regression yielded 18 proteins, from which a risk score was constructed to dichotomize prostate cancer patients into low- and high-risk groups. This 18-protein signature is prognostic for the risk of biochemical recurrence and completely independent of the intermediate GG. Our results suggest that markers generated by computational proteomic profiling have the potential for clinical applications including integration into prostate cancer management.

'High proliferative cribriform prostate cancer' defines a patient subgroup with an inferior prognosis

AIMS

A cribriform pattern, reactive stroma (RS), PTEN, Ki67 and ERG are promising prognostic biomarkers in primary prostate cancer (PCa). We aim to determine the relative contribution of these factors and the Cancer of the Prostate Risk Assessment Postsurgical (CAPRA-S) score in predicting PCa prognosis.

METHODS AND RESULTS

We included 475 patients who underwent radical prostatectomy (2010-12, median follow-up = 8.7 years). Cribriform pattern was identified in 57% of patients, PTEN loss in 55%, ERG expression in 51%, RS in 39% and high Ki67 in 9%. In patients with multiple samples from the same malignant focus and either PTEN loss or high Ki67, intrafocal heterogeneity for PTEN and Ki67 expression was detected in 55% and 89%, respectively. In patients with samples from two or more foci, interfocal heterogeneity was detected in 46% for PTEN and 6% for Ki67. A cribriform pattern and Ki67 were independent predictors of biochemical recurrence (BCR) and clinical recurrence (CR), whereas ERG expression was an independent predictor of CR. Besides CAPRA-S, a cribriform pattern provided the highest relative proportion of explained variation for predicting BCR (11%), and Ki67 provided the highest relative proportion of explained variation for CR (21%). In patients with a cribriform pattern, high Ki67 was associated with a higher risk of BCR [hazard ratio (HR) = 2.83, P < 0.001] and CR (HR = 4.35, P < 0.001).

CONCLUSIONS

High Ki67 in patients with a cribriform pattern identifies a patient subgroup with particularly poor prognosis, which we termed 'high proliferative cribriform prostate cancer'. These results support reporting a cribriform pattern in pathology reports, and advocate implementing Ki67.

Deep learning approach for accurate prostate cancer identification and stratification using combined immunostaining of cytokeratin, p63, and racemase

BACKGROUND

Prostate cancer (PCa) is the most frequently diagnosed cancer in men worldwide, affecting around 1.4 million individuals. Current PCa diagnosis relies on histological analysis of prostate biopsy samples, an activity that is both time-consuming and prone to observer bias. Previous studies have demonstrated that immunostaining of cytokeratin, p63, and racemase can significantly improve the sensitivity and the specificity of PCa detection compared to traditional H&E staining.

METHODS

This study introduces a novel approach that combines diagnosis-specific immunohistochemical (IHC) staining and deep learning techniques to provide reliable stratification of prostate glands. Our approach leverages a customized segmentation network, called K-PPM, that incorporates adaptive kernels and multiscale feature integration to enhance the functional information of IHC. To address the high class-imbalance problem in the dataset, we propose a weighted adaptive patch-extraction and specific-class kernel update.

RESULTS

Our system achieved noteworthy results, with a mean Dice Score Coefficient of 90.36% and a mean absolute error of 1.64 % in specific-class gland quantification on whole slides. These findings demonstrate the potential of our system as a valuable support tool for pathologists, reducing workload and decreasing diagnostic inter-observer variability.

CONCLUSIONS

Our study presents innovative approaches that have broad applicability to other digital pathology areas beyond PCa diagnosis. As a fully automated system, this model can serve as a framework for improving the histological and IHC diagnosis of other types of cancer.

Reprint of: morphologic spectrum of treatment-related changes in prostate tissue and prostate cancer: an updated review

A wide range of treatment options are available to patients with prostate cancer. Some treatments are standard (currently used) while some are emerging therapies. Androgen deprivation therapy is typically reserved for localized or metastatic prostate cancer not amenable to surgery. Radiation therapy may be offered to individuals for local therapy with curative intent in low- or intermediate-risk disease that may have a high probability of progression on active surveillance or where surgery is not suitable. Focal therapy/ablation treatment is an alternative approach for those who prefer to avoid radical prostatectomy for localized disease of low- or intermediate-risk or as salvage therapy after failed radiation therapy. Chemotherapy and immunotherapy remain under investigation and are currently used for androgen-independent disease or hormone-refractory prostate cancer; however, a better understanding of therapeutic efficacy is needed. Histopathologic changes observed in benign and malignant prostate tissue induced by hormonal therapies and radiation therapy are well described, whereas treatment-related effects secondary to novel therapies continue to be documented although their clinical significance is not absolutely clear. An informed and accurate evaluation of post-treatment prostate specimens requires pathologists with diagnostic acumen and knowledge relating to the histopathologic spectrum associated with each treatment option. In situations when clinical history is lacking, but morphologic features are suggestive of prior treatment, pathologists are encouraged to consult clinical colleagues regarding prior treatment history including details of when treatment was initiated and duration of therapy. This review aims to provide a concise update of current and emerging therapies for prostate cancer, histologic alterations and recommendations on Gleason grading.

Intermediate-risk Prostate Cancer-A Sheep in Wolf's Clothing?

This case-based discussion describes a 65-year-old man newly diagnosed with International Society of Urological Pathology (ISUP) grade 2 prostate cancer (PCa). According to the European Association of Urology classification system, the patient harbors an intermediate-risk cancer. In step-by step discussion, we elaborate guideline-based treatment modalities for intermediate-risk PCa focused on debating active surveillance versus active treatment. Thereby, we discuss the importance of patient characteristics, including age, hereditary factors, life expectancy and comorbidity status, findings of multiparametric magnetic resonance imaging, as well as prostate-specific antigen (PSA) density and PSA kinetics, in predicting the clinical course of the disease. In addition, we focus on cribriform pathology as a predictor of adverse outcomes and critically discuss its relevance in patient management. Lastly, we outline genomic stratification in ISUP 2 cancer as a future tool to predict PCa aggressiveness. PATIENT SUMMARY: Based on current guidelines, patients with intermediate-risk prostate cancer are treated actively or can alternatively undergo an active surveillance approach when favorable risk factors are present. One major issue is to discriminate between patients who benefit from an active therapy approach and those who benefit from a deferred treatment. Therefore, reliable biomarkers and early predictors of disease progression are needed urgently.

Transcriptomic Features of Cribriform and Intraductal Carcinoma of the Prostate

BACKGROUND

Cribriform (CF) and/or intraductal carcinoma (IDC) are associated with more aggressive prostate cancer (CaP) and worse outcomes.

OBJECTIVE

The transcriptomic features that typify CF/IDC are not well described and the capacity for clinically utilized genomic classifiers to improve risk modeling for CF/IDC remains undefined.

DESIGN, SETTING, AND PARTICIPANTS

We performed a retrospective review of CaP patients who had Decipher testing at a single high-volume institution. Index lesions from radical prostatectomy specimens were identified by genitourinary pathologists who simultaneously reviewed prostatectomy specimens for the presence of CF and IDC features. Patients were grouped based on pathologic features, specifically the absence of CF/IDC (CF-/IDC-), CF positive only (CF+/IDC-), and CF/IDC positive (CF+/IDC+).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Clinical, pathologic, and genomic categorical variables were assessed using the Pearson chi-square test, while quantitative variables were assessed with the Kruskal-Wallis test. Multivariable logistic regression was used to identify the predictors of high-risk Decipher scores (>0.60). A gene set enrichment analysis was performed to identify genes and gene networks associated with CF/IDC status.

RESULTS AND LIMITATIONS

A total of 463 patients were included. Patients who were CF+/IDC+ had the highest Decipher risk scores (CF+/IDC+: 0.79 vs CF+/IDC-: 0.71 vs CF-/IDC-: 0.56, p < 0.001). On multivariate logistic regression, predictors of high-risk Decipher scores included the presence of CF, both alone (CF+/IDC-; odds ratio [OR]: 5.45, p < 0.001) or in combination with positive IDC status (CF+/IDC+; OR: 6.87, p < 0.001). On the gene set enrichment analysis, MYC pathway upregulation was significantly enriched in tumor samples from CF/IDC-positive patients (normalized enrichment score [NES]: 1.65, p = 0.046). Other enriched pathways included E2F targets (NES: 1.69, p = 0.031) and oxidative phosphorylation (NES: 1.68, =0 .033).

CONCLUSIONS

This is the largest series identifying an association between a clinically validated genomic classifier and the presence of CF and IDC at radical prostatectomy. Tumors with CF and intraductal features were associated with aggressive transcriptomic signatures.

PATIENT SUMMARY

Genomic-based tests are becoming readily available for the management of prostate cancer. We observed that Decipher, a commonly used genomic test in prostate cancer, correlates with unfavorable features in tissue specimens.

Enrichment of "Cribriform" morphologies (intraductal and cribriform adenocarcinoma) and genomic alterations in radiorecurrent prostate cancer

Locally relapsed prostate cancer (PCa) after radiation therapy (RT) is associated with substantial morbidity and mortality. Morphological and molecular consequences that may contribute to RT resistance and local recurrence remain poorly understood. Locally recurrent PCa tissue from 53 patients with clinically localized PCa who failed with primary RT and subsequently underwent salvage radical prostatectomy (RP) was analyzed for tumor focality, clinicopathological, molecular, and genomic characteristics. Targeted next-generation sequencing with full exon coverage of 1,425 cancer-related genes was performed on 10 representative radiorecurrent PCas exhibiting no RT effect with matched adjacent benign prostate tissue. At RP, 37 (70%) of PCas had no RT effect with the following characteristics: grade group (GG) ≥ 3 (70%), unifocal tumor (75%), extraprostatic disease (78%), lymph node metastasis (8%), and "cribriform" morphologies (84%) [cribriform PCa (78%) or intraductal carcinoma (IDC-P) (61%)] at a median percentage of approximately 80% of tumor volume. In the setting of multifocal tumors (25%) at RP, the cribriform morphologies were restricted to index tumors. Of 32 patients with available pre-RT biopsy information, 16 had GG1 PCa, none had cribriform morphologies at baseline but 81% demonstrated cribriform morphologies at RP. Notable alterations detected in the sequenced tumors included: defects in DNA damage response and repair (DDR) genes (70%) (TP53, BRCA2, PALB2, ATR, POLQ), PTEN loss (50%), loss of 8p (80%), and gain of MYC (70%). The median tumor mutational burden was 4.18 mutations/Mb with a range of 2.16 to 31.86. Our findings suggest that most radiorecurrent PCas are enriched in cribriform morphologies with potentially targetable genomic alterations. Understanding this phenotypic and genotypic diversity of radiorecurrent PCa is critically important to facilitate optimal patient management.

Morphologic spectrum of treatment-related changes in prostate tissue and prostate cancer: An Updated Review

A wide range of treatment options is available to patients with prostate cancer. Some treatments are standard (currently used) while some are emerging therapies. Androgen deprivation therapy is typically reserved for localized or metastatic prostate cancer not amenable to surgery. Radiation therapy may be offered to individuals for local therapy with curative intent in low- or intermediate-risk disease that may have a high probability of progression on active surveillance or where surgery is not suitable. Focal therapy/ablation treatment is an alternative approach for those who prefer to avoid radical prostatectomy for localized disease of low- or intermediate-risk or as salvage therapy following failed radiation therapy. Chemotherapy and immunotherapy remain under investigation and are currently used for androgen-independent disease or hormone-refractory prostate cancer; however a better understand therapeutic efficacy is needed. Histopathologic changes observed in benign and malignant prostate tissue induced by hormonal therapies and radiation therapy is well described, while treatment-related effects secondary to novel therapies continue to be documented although their clinical significance is not absolutely clear. An informed and accurate evaluation of post-treatment prostate specimens requires pathologists with diagnostic acumen and knowledge relating to the histopathologic spectrum associated with each treatment option. In situations when clinical history is lacking, but morphologic features are suggestive of prior treatment, pathologists are encouraged to consult clinical colleagues regarding prior treatment history including details of when treatment was initiated and duration of therapy. This review aims to provide a concise update of current and emerging therapies for prostate cancer, histologic alterations and recommendations on Gleason grading.

Large and small cribriform architecture have similar adverse clinical outcome on prostate cancer biopsies

Aims

Invasive cribriform and intraductal carcinoma (IDC) are associated with adverse outcome in prostate cancer patients, with the large cribriform pattern having the worst outcome in radical prostatectomies. Our objective was to determine the impact of the large and small cribriform patterns in prostate cancer biopsies.

Methods and results

Pathological revision was carried out on biopsies of 1887 patients from the European Randomised Study of Screening for Prostate Cancer. The large cribriform pattern was defined as having at least twice the size of adjacent benign glands. The median follow‐up time was 13.4 years. Hazard ratios for metastasis‐free survival (MFS) and disease‐specific survival (DSS) were calculated using Cox proportional hazards regression. Any cribriform pattern was found in 280 of 1887 men: 1.1% IDC in grade group (GG) 1, 18.2% in GG2, 57.1% in GG3, 55.4% in GG4 and 59.3% in GG5; the large cribriform pattern was present in 0, 0.5, 9.8, 18.1 and 17.3%, respectively. In multivariable analyses, small and large cribriform patterns were both (P < 0.005) associated with worse MFS [small: hazard ratio (HR) = 3.04, 95% confidence interval (CI) = 1.93–4.78; large: HR = 3.17, 95% CI = 1.68–5.99] and DSS (small: HR = 4.07, 95% CI = 2.51–6.62; large: HR = 4.13, 95% CI = 2.14–7.98). Patients with the large cribriform pattern did not have worse MFS (P = 0.77) or DSS (P = 0.96) than those with the small cribriform pattern.

Conclusions

Both small and large cribriform patterns are associated with worse MFS and DSS in prostate cancer biopsies. Patients with the large cribriform pattern on biopsy have a similar adverse outcome as those with the small cribriform pattern.

Hidden clues in prostate cancer - Lessons learned from clinical and pre-clinical approaches on diagnosis and risk stratification

The heterogeneity of prostate cancer is evident at clinical, morphological and molecular levels. To aid clinical decision making, a three-tiered system for risk stratification is used to designate low-, intermediate-, and high-risk of disease progression. Intermediate-risk prostate cancers are the most frequently diagnosed, and even with common diagnostic features, can exhibit vastly different clinical progression. Thus, improved risk stratification methods are needed to better predict patient outcomes. Here, we provide an overview of the improvements in diagnosis/prognosis arising from advances in pathology reporting of prostate cancer, which can improve risk stratification, especially for patients with intermediate-risk disease. This review discusses updates to pathology reporting of morphological growth patterns, and proposes the utility of integrating prognostic biomarkers or innovative imaging techniques to enhance clinical decision-making. To complement clinical studies, experimental approaches using patient-derived tumors have highlighted important cellular and morphological features associated with aggressive disease that may impact treatment response. The intersection of urology, pathology and scientific disciplines is required to work towards a common goal of understanding disease pathogenesis, improving the stratification of patients with intermediate-risk disease and subsequently defining optimal treatment strategies using precision-based approaches.

Cribriform architecture outperforms percent Gleason pattern 4 and tertiary pattern 5 in predicting outcome of Grade group 2 prostate cancer patients

Aims

Gleason pattern 4 (GP4) percentage, invasive cribriform and/or intraductal carcinoma (IC/IDC) and the presence of tertiary Gleason pattern 5 (TP5) in radical prostatectomy (RP) specimens all aid in the risk stratification of Grade Group (GG) 2 prostate cancer patients. However, it is unclear to what extent these pathological features are mutually related and what are their individual values if they are investigated simultaneously. The aims of this study were: (i) to determine the mutual relationships of the GP4 percentage, IC/IDC and TP5 in GG2 RP specimens; and (ii) to assess their prognostic value for biochemical recurrence‐free survival (BCRFS).

Methods and results

Of 1064 RP specimens, 472 (44.4%) showed GG2 prostate cancer. Patients with ≥25% GP4 more frequently had IC/IDC (67.0% versus 43.9%; P < 0.001) and TP5 (20.6% versus 5.8%; P < 0.001) than those with <25% GP4. In unadjusted analysis, an increased GP4 percentage [hazard ratio (HR) 1.3; 95% confidence interval (CI) 1.0–1.6; P = 0.04] and IC/IDC (log rank P < 0.001) were associated with shorter BCRFS, whereas TP5 (P = 0.12) and a dichotomised (<25%, ≥25%) GP4 percentage (P = 0.10) were not. In multivariable analysis, IC/IDC was an independent prognostic factor (HR 1.9; 95% CI 1.2–2.9; P = 0.005) for BCRFS, whereas a continuous or dichotomised GP4 percentage and TP5 were not independent prognostic factors.

Conclusion

In conclusion, a higher GP4 percentage in RP specimens was associated with more frequent IC/IDC and TP5. IC/IDC was an independent predictor for BCRFS, whereas the GP4 percentage and TP5 were not. These findings underscore the importance of routinely including the presence of IC/IDC in RP pathology reports.

Improving the prediction of biochemical recurrence after radical prostatectomy with the addition of detailed pathology of the positive surgical margin and cribriform growth

The risk on biochemical recurrence (BCR) after radical prostatectomy (RP) is usually estimated using PSA and pathological stage and grading including the presence of positive surgical margins (PSM). Objective was to investigate whether the presence of cribriform growth in the primary tumor, Grade Group (GG) at the PSM, and length of the PSM have added value in the prognostication. We analyzed data of 835 patients initially treated with RP between 2000 and 2017. Cox regression models were developed to compare the baseline model (PSA, pT-stage, pN-stage, GG at RP, and presence of PSM) with an extended model (adding the presence of cribriform growth, length and GG at the PSM) using the likelihood ratio test. Discrimination was assessed at internal validation by the time-dependent area under the receiver operating characteristic curve (AUC) at 3- and 5-year. A total of 224 men experienced BCR. Median follow-up for men without BCR was 50.4 months (interquartile range, IQR 11.9-95.5). The extended model had a significant better fit, χ²(4) = 31.0, p < 0.001 than the baseline model. The AUC of the 3- and 5-year extended model was 0.85 (95% CI 0.81-0.88) compared to 0.83 (95% CI 0.79-0.87) for the baseline model. Importantly, the presence of cribriform growth in the primary tumor, and GG ≥ 2 at PSM were associated with a higher risk on BCR. In conclusion, the addition of pathological variables improved the prediction of the risk on BCR after RP slightly. However, the clinical implications of this model are important.

Diagnosis of "cribriform" prostatic adenocarcinoma: an interobserver reproducibility study among urologic pathologists with recommendations

Accurate diagnosis of cribriform Gleason pattern 4 (CrP4) prostate adenocarcinoma (PCa) is important due to its independent association with adverse clinical outcomes and as a growing body of evidence suggests that it impacts clinical decision making in PCa management. To identify reproducible features for diagnosis of CrP4, we assessed interobserver agreement among 27 experienced urologic pathologists of 60 digital images from 44 radical prostatectomies (RP) that represented a broad spectrum of potential CrP4. The following morphologic features were correlated with the consensus diagnosis (defined as 75% agreement) for each image: partial vs. transluminal glandular bridging, intraglandular stroma, <12 vs. ≥12 lumina, well vs. poorly formed lumina, mucin (mucinous fibroplasia, extravasation, or extracellular pool), size (compared to benign glands and number of lumina), number of attachments with gland border by tumor cells forming a "glomeruloid-like" pattern, a clear luminal space along the periphery of gland occupying <50% of glandular circumference, central nerve, dense (cell mass occupying >50% of luminal space) vs. loose, and regular vs. irregular contour. Interobserver reproducibility for the overall diagnostic agreement was fair (k=0.40). Large CrP4 had better agreement (k=0.49) compared to small CrP4 (k=0.40). Transluminal bridging, dense cellular proliferation, a clear luminal space along the periphery of gland occupying <50% of gland circumference, lack of intraglandular mucin, and lack of contact between the majority of intraglandular cells with stroma were significantly associated with consensus for CrP4. In contrast, partial bridging, majority of intraglandular cells in contact with stroma, mucinous fibroplasia, only one attachment to the gland border by tumor cells forming a "glomeruloid-like" pattern, and a clear luminal space along the periphery of gland accounting for >50% of the glandular circumference were associated with consensus against CrP4. In summary, we identified reproducible morphological features for and against CrP4 diagnosis, which could be used to refine and standardize the diagnostic criteria for CrP4.

ISUP Consensus Definition of Cribriform Pattern Prostate Cancer

The presence of a cribriform pattern is now recognized as a clinically important, independent adverse prognostic indicator for prostate cancer. For this reason the International Society of Urological Pathology (ISUP) recently recommended its inclusion in standard reporting. In order to improve interobserver agreement as to the diagnosis of cribriform patterns, the ISUP assembled an international panel of 12 expert urogenital pathologists for the purpose of drafting a consensus definition of cribriform pattern in prostate cancer, and provide their opinions on a set of 32 images and on potential diagnostic criteria. These images were selected by the 2 nonvoting convenors of the study and included the main categories where disagreement was anticipated. The Delphi method was applied to promote consensus among the 12 panelists in their review of the images during 2 initial rounds of the study. Following a virtual meeting, convened to discuss selected images and diagnostic criteria, the following definition for cribriform pattern in prostate cancer was approved: "A confluent sheet of contiguous malignant epithelial cells with multiple glandular lumina that are easily visible at low power (objective magnification ×10). There should be no intervening stroma or mucin separating individual or fused glandular structures" together with a set of explanatory notes. We believe this consensus definition to be practical and that it will facilitate reproducible recognition and reporting of this clinically important pattern commonly seen in prostate cancer. The images and the results of the final Delphi round are available at the ISUP website as an educational slide set (https://isupweb.org/isup/blog/slideshow/cribriform-slide-deck/).

Future of biomarker evaluation in the realm of artificial intelligence algorithms: application in improved therapeutic stratification of patients with breast and prostate cancer

Clinical workflows in oncology depend on predictive and prognostic biomarkers. However, the growing number of complex biomarkers contributes to costly and delayed decision-making in routine oncology care and treatment. As cancer is expected to rank as the leading cause of death and the single most important barrier to increasing life expectancy in the 21st century, there is a major emphasis on precision medicine, particularly individualisation of treatment through better prediction of patient outcome. Over the past few years, both surgical and pathology specialties have suffered cutbacks and a low uptake of pathology specialists means a solution is required to enable high-throughput screening and personalised treatment in this area to alleviate bottlenecks. Digital imaging in pathology has undergone an exponential period of growth. Deep-learning (DL) platforms for hematoxylin and eosin (H&E) image analysis, with preliminary artificial intelligence (AI)-based grading capabilities of specimens, can evaluate image characteristics which may not be visually apparent to a pathologist and offer new possibilities for better modelling of disease appearance and possibly improve the prediction of disease stage and patient outcome. Although digital pathology and AI are still emerging areas, they are the critical components for advancing personalised medicine. Integration of transcriptomic analysis, clinical information and AI-based image analysis is yet an uncultivated field by which healthcare professionals can make improved treatment decisions in cancer. This short review describes the potential application of integrative AI in offering better detection, quantification, classification, prognosis and prediction of breast and prostate cancer and also highlights the utilisation of machine learning systems in biomarker evaluation.

A distinct repertoire of cancer-associated fibroblasts is enriched in cribriform prostate cancer

Outcomes for men with localized prostate cancer vary widely, with some men effectively managed without treatment on active surveillance, while other men rapidly progress to metastatic disease despite curative-intent therapies. One of the strongest prognostic indicators of outcome is grade groups based on the Gleason grading system. Gleason grade 4 prostate cancer with cribriform morphology is associated with adverse outcomes and can be utilized clinically to improve risk stratification. The underpinnings of disease aggressiveness associated with cribriform architecture are not fully understood. Most studies have focused on genetic and molecular alterations in cribriform tumor cells; however, less is known about the tumor microenvironment in cribriform prostate cancer. Cancer-associated fibroblasts (CAFs) are a heterogeneous population of fibroblasts in the tumor microenvironment that impact cancer aggressiveness. The overall goal of this study was to determine if cribriform prostate cancers are associated with a unique repertoire of CAFs. Radical prostatectomy whole-tissue sections were analyzed for the expression of fibroblast markers (ASPN in combination with FAP, THY1, ENG, NT5E, TNC, and PDGFRβ) in stroma adjacent to benign glands and in Gleason grade 3, Gleason grade 4 cribriform, and Gleason grade 4 noncribriform prostate cancer by RNAscope®. Halo® Software was used to quantify percent positive stromal cells and expression per positive cell. The fibroblast subtypes enriched in prostate cancer were highly heterogeneous. Both overlapping and distinct populations of low abundant fibroblast subtypes in benign prostate stroma were enriched in Gleason grade 4 prostate cancer with cribriform morphology compared to Gleason grade 4 prostate cancer with noncribriform morphology and Gleason grade 3 prostate cancer. In addition, gene expression was distinctly altered in CAF subtypes adjacent to cribriform prostate cancer. Overall, these studies suggest that cribriform prostate cancer has a unique tumor microenvironment that may distinguish it from other Gleason grade 4 morphologies and lower Gleason grades.

Analysis of Prostate Adenocarcinoma Histopathological Types in Relation to Tumor Grade

Prostate adenocarcinomas are some of the most common malignancies diagnosed in men, and the evaluation of tumor growth patterns is the basis for establishing the aggressiveness of the lesions. The study included 283 cases of prostate adenocarcinomas for which histopathological type and tumor grade were analyzed. The results indicated the association of ductal, sarcomatoid and signet ring-like cell types with aggressive growth patterns and high scores, atrophic and pseudohyperplastic types with mild growth patterns and low scores, foamy gland type presented intermediate growth patterns/scores, while conventional and colloid types had variable aspects. The grading systems used may be considered consistent with the histological types of prostate adenocarcinomas.

Prostate cancer growth patterns beyond the Gleason score: entering a new era of comprehensive tumour grading

The Gleason grading system is one of the most important factors in clinical decision‐making for prostate cancer patients, and is entirely based on the classification of tumour growth patterns. In recent years it has become clear that some individual growth patterns themselves have independent prognostic value, and could be used for better personalised risk stratification. In this review we summarise recent literature on the clinicopathological value and molecular characteristics of individual prostate cancer growth patterns, and show how these, most particularly cribriform architecture, could alter treatment decisions for prostate cancer patients.

Automated detection of cribriform growth patterns in prostate histology images

Cribriform growth patterns in prostate carcinoma are associated with poor prognosis. We aimed to introduce a deep learning method to detect such patterns automatically. To do so, convolutional neural network was trained to detect cribriform growth patterns on 128 prostate needle biopsies. Ensemble learning taking into account other tumor growth patterns during training was used to cope with heterogeneous and limited tumor tissue occurrences. ROC and FROC analyses were applied to assess network performance regarding detection of biopsies harboring cribriform growth pattern. The ROC analysis yielded a mean area under the curve up to 0.81. FROC analysis demonstrated a sensitivity of 0.9 for regions larger than \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${0.0150}\,\hbox {mm}^{2}$$\end{document}0.0150mm2 with on average 7.5 false positives. To benchmark method performance for intra-observer annotation variability, false positive and negative detections were re-evaluated by the pathologists. Pathologists considered 9% of the false positive regions as cribriform, and 11% as possibly cribriform; 44% of the false negative regions were not annotated as cribriform. As a final experiment, the network was also applied on a dataset of 60 biopsy regions annotated by 23 pathologists. With the cut-off reaching highest sensitivity, all images annotated as cribriform by at least 7/23 of the pathologists, were all detected as cribriform by the network and 9/60 of the images were detected as cribriform whereas no pathologist labelled them as such. In conclusion, the proposed deep learning method has high sensitivity for detecting cribriform growth patterns at the expense of a limited number of false positives. It can detect cribriform regions that are labelled as such by at least a minority of pathologists. Therefore, it could assist clinical decision making by suggesting suspicious regions.